|
Last data update: 2014.03.03
|
R: Self-concordant empirical likelihood for a vector mean
Self-concordant empirical likelihood for a vector mean
Description
Self-concordant empirical likelihood for a vector mean
Usage
emplik(dat, mu = rep(0, ncol(dat)), lam = rep(0, ncol(dat)),
eps = 1/nrow(dat), M = 1e+30, thresh = 1e-30, itermax = 100)
Arguments
dat |
n by d matrix of d-variate observations
|
mu |
d vector of hypothesized mean of dat
|
lam |
starting values for Lagrange multiplier vector, default to zero vector
|
eps |
lower cutoff for -log, with default 1/nrow(dat)
|
M |
upper cutoff for -log.
|
thresh |
convergence threshold for log likelihood (default of 1e-30 is agressive)
|
itermax |
upper bound on number of Newton steps.
|
Value
a list with components
#'
-
logelr log empirical likelihood ratio.
-
lam Lagrange multiplier (vector of length d).
-
wts n vector of observation weights (probabilities).
-
conv boolean indicating convergence.
-
niter number of iteration until convergence.
-
ndec Newton decrement.
-
gradnorm norm of gradient of log empirical likelihood.
Author(s)
Art Owen, C++ port by Leo Belzile
References
Owen, A.B. (2013). Self-concordance for empirical likelihood, Canadian Journal of Statistics, 41(3), 387–397.
Results
|
providing 
.